Gd‐DTPA bolus tracking in the myocardium using <i>T</i><sub>1</sub> fast acquisition relaxation mapping (<i>T</i><sub>1</sub> FARM)
Bibliographic record
Abstract
MRI methods currently used for bolus tracking in the myocardium, such as saturation recovery turbo-fast low-angle shot (FLASH) (srTFL), are limited by signal intensity (SI) saturation at high contrast agent (CA) concentrations. By using T1 fast acquisition relaxation mapping (T1 FARM), a Gd-DTPA bolus (0.075 vs. 0.025 mmol/kg) may be injected without causing saturation. This study tested the feasibility of in vivo T1 FARM bolus tracking under rest/stress conditions in seven beagles with multiple permanently occluded branches of the left anterior descending (LAD) coronary artery. Although it underestimated the myocardial perfusion reserve (MPR) measured ex vivo using radioactive microspheres (mean +/- SEM; 3.60 +/- 0.26), the MPR determined upon application of the modified Kety model (1.86 +/- 0.10) enabled distinction between normal and infarcted tissue. The partition coefficient (lambda) estimated at rest and stress using the modified Kety model underestimated ex vivo radioactive measurements in infarcted tissue (0.25 +/- 0.01 vs. 0.26 +/- 0.01 vs. 0.79 +/- 0.08 ml/g, P < 0.0001) yet was accurate in normal tissue (0.28 +/- 0.01 vs. 0.30 +/- 0.01 vs. 0.33 +/- 0.01 ml/g, P = NS). Thus, although unsuitable for myocardial viability assessment, T1 FARM bolus tracking shows potential for assessment of myocardial perfusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".